Mass-Spring-Damper simulation using a model based on Python, Boost or Cython. Coefficient estimation is performed using a number of techniques including linear regression, gradient-based optimisation and sampling.

It's been a long time since the last post! Lately I've been playing around with mapping frameworks and services. Click through for a brief introduction on using Mapbox to create beatutiful layered maps.

I thought I should post something on an area I've been working in more recently, so here is my simulation of the Simultaneous Localisation and Mapping (SLAM) technique, as applied to an autonomous vehicle navigating through waypoints across a feature-rich terrain.

I dug up some more old VRML simulation replays created some time ago. These ones demonstrate the dynamic response of CH-47B Chinook and UH-1H helicopters with externally slung loads undergoing a either simple disturbance or manoeuvre. The coupled-body dynamic simulation was performed in Matlab and then exported to VRML for visualisation.

Now that the multiprocessing library comes standard in Python 2.6, I thought I'd migrate some of my apps to take full advantage. However, there aren't many examples out there showing how to write a basic multiprocessing program with a graphical front-end. In order to prototype the program design, I wrote a simple wxPython script.